An investigation of nature inspired algorithms on a particular vehicle routing problem in the presence of shift assignment

ALP G., ALKAYA A. F.

COMPUTERS & OPERATIONS RESEARCH, cilt.141, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 141
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.cor.2021.105685
  • Dergi Adı: COMPUTERS & OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Workforce scheduling, Vehicle routing, Dynamic neighbour generation, Multi objective optimization, Evolutionary algorithms, MIGRATING BIRDS OPTIMIZATION
  • Marmara Üniversitesi Adresli: Evet

Özet

Scheduling and routing processes are indispensable while planning most of the business operations. These processes that can help to improve each other's efficiency are generally treated separately. This paper brings workforce scheduling and vehicle routing problems together in a way that has never been done before and consequently vehicle routing problem in the presence of shift assignment (VRP_SA) is introduced to the literature. After building the mathematical model of the problem it is verified on a solver and then real large-sized instances taken from a company are solved using a set of evolutionary algorithms. Three novel solution techniques are introduced based on our framework called dynamic neighbour generation. As one of the contributions of this study, dynamic neighbour generation framework may easily be extended to include other multi objective optimization algorithms to increase their exploration capability, and thus it offers an alternative development facility for solving multi objective optimization problems. Results of computational experiments show that the proposed framework definitely offers promising and robust results in large sized problem instances in terms of hypervolume and inverted generational distance indicators.